Evaluating Deep Learning Approaches for Covid19 Fake News Detection

Abstract

Social media platforms like Facebook, Twitter, and Instagram have enabled connection and communication on a large scale. It has revolutionized the rate at which information is shared and enhanced its reach. However, another side of the coin dictates an alarming story. These platforms have led to an increase in the creation and spread of fake news. The fake news has not only influenced people in the wrong direction but also claimed human lives. During these critical times of the Covid19 pandemic, it is easy to mislead people and make them believe in fatal information. Therefore it is important to curb fake news at source and prevent it from spreading to a larger audience. We look at automated techniques for fake news detection from a data mining perspective. We evaluate different supervised text classification algorithms on Contraint@AAAI 2021 Covid-19 Fake news detection dataset. The classification algorithms are based on Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). We also evaluate the importance of unsupervised learning in the form of language model pre-training and distributed word representations using unlabelled covid tweets corpus. We report the best accuracy of 98.41\% on the Covid-19 Fake news detection dataset.

arXiv commentAccepted at Contraint@AAAI 2021

References in corpus (5)

• Sequence to Sequence Learning with Neural Networks
• Fake News Detection on Social Media: A Data Mining Perspective
• Fighting an Infodemic: COVID-19 Fake News Dataset
• COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter
• Beyond News Contents: The Role of Social Context for Fake News Detection

Cited by in corpus (10)

• A Proposed Bi-LSTM Method to Fake News Detection
• Indonesia's Fake News Detection using Transformer Network
• A Survey of COVID-19 Misinformation: Datasets, Detection Techniques and Open Issues
• Hierarchical Neural Network Approaches for Long Document Classification
• Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19
• A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models
• On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations
• L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset
• Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives
• Contextual Hate Speech Detection in Code Mixed Text using Transformer Based Approaches